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Record W4415586416 · doi:10.21083/crrf.v29i1.7645

Public Benefit from Public Resources?: Understanding the Changing Conditions for Rural Resource Regions

2025· article· W4415586416 on OpenAlexaff
Sean Markey, Greg Halseth, Laura Ryser

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsUniversity of Northern British ColumbiaSimon Fraser University
Fundersnot available
KeywordsResource (disambiguation)Redistribution (election)Presentation (obstetrics)PoliticsCapital (architecture)Resource allocationRural areaResource distribution

Abstract

fetched live from OpenAlex

The purpose of this presentation is to understand how institutional processes at the national or sub-national level contribute to making rural resource regions attractive places for capital and labour through the redistribution of royalties/revenues from resource industries. The presentation will outline how different political and economic contexts shape how resource royalties/revenues are collected and distributed back into the region from which the resources have been extracted, and how effectively resource royalties strengthen the regional economy. Drawing from a theoretical foundation of staples theory, evolutionary economic geography and new regionalism, this presentation will examine the relationship between the state and resource regions. In particular the presentation will seek to engage the CRRF audience with a more detailed understanding of: (i) the political economy of the redistributive policy mechanisms used to support economic development in rural resource peripheries, (ii) how and why these resource royalty regimes have evolved (and changed) over time, and (iii) how royalty funds are collected and used at the regional and local level to support community and regional economic development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.241
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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